Core Viewpoint - The article discusses the implications of the China Securities Regulatory Commission's "Action Plan for Promoting the High-Quality Development of Public Funds," particularly focusing on the constraints of performance benchmarks for fund managers and the potential impact on their investment strategies and fee structures [1]. Group 1: Market Overestimation of Active Equity Funds' Underperformance - The market has overestimated the proportion of active equity funds that will underperform their benchmarks by 10% from 2022 to 2024, with 68.76% projected to face this issue, compared to only 1.05% from 2019 to 2021 [2]. - The first method of measuring this probability assumes fund managers do not adjust their benchmarks, which may not provide a reliable future reference due to historical negligence towards benchmarks [2]. - The second method assumes fund managers will align their benchmarks with broad indices like the CSI 800, but this also presents challenges as managers typically select benchmarks that suit their investment styles [5]. Group 2: Benchmark Selection Challenges - If fund managers choose broad indices like the CSI 300 or CSI 800 without altering their investment strategies, the probability of underperforming the benchmark by over 10% becomes uncontrollable [8]. - Fund managers face two choices: either align with broad indices and adjust their portfolios accordingly or select benchmarks that match their investment styles, effectively turning their products into "enhanced index funds" [8]. - The analysis shows that if managers select benchmarks aligned with their styles, the proportion of funds underperforming by 10% drops significantly from 47.82% to 22.34% [10][11]. Group 3: Short-Term Market Expectations - The market is currently estimating the gaps between fund allocations and benchmark indices, which may lead to short-term trading opportunities in certain sectors [19]. - Active equity funds are generally underweight in financials and traditional consumer sectors while overweight in technology and growth sectors, indicating a need for adjustments if broad indices are chosen as benchmarks [20][21]. Group 4: Benchmark Selection for Active Equity Funds - The article emphasizes the importance of selecting appropriate benchmarks for active equity funds, suggesting that the choice of benchmark should align with the fund manager's investment style rather than conforming to broad indices [29]. - Major index providers have developed a range of indices that cover various strategies, with broad indices like the CSI 300 and CSI 800 being the most commonly selected benchmarks [29][31]. - The article outlines that the characteristics of indices, such as industry distribution and stock selection methods, should be analyzed to ensure they reflect the performance of public funds accurately [40][43].

以下文章来源于兴证全球基金 ，作者与您相伴的 最近市场情绪高涨， 身边 " 后悔没定投 " 的声音更响了：有人看着别人前两 年定投的收益跟着行情反弹，自己却因去年不敢入场踏空；有人懊恼 2021 年 高点后没靠定投摊薄成本，如今只能看着别人回本盈利。大家终于发现，那些 在震荡期悄悄攒下的 " 筹码 " ，恰恰在市场回暖时成了最踏实的收益。今天我 们就用数据说话，聊聊定投的价值。 一个关于定投的故事 1997年6月，一位投资者每月定投1000美元买入邓普顿泰国基金，随后亚洲金融风暴袭来， 1 5 个月后 基金净值从 10美元跌至2.22美元，跌幅近80%。 两年后， 基金净值仅仅回升至 6.13美元，但得益于定投， 他不仅没亏，还赚了 41% 。 那些在低谷攒下的低价份额，成了后续反弹的关键。 兴证全球基金 . 投资理财，有温度，有深度，有态度。 这不是个例。假设有人从 2015年6月A股 上证指数 5178点高点 开始， 入场定投偏股基金指数，熬过几年震荡，六年后 ，市场再度回暖，最终 收益率 可 达 72%，跑赢同期上证综指102个百分点。 数据来源 ：Choice ，201 5/6/ 12-2021/6/ ...

Core Insights - The total trading volume of the CSI 800 Index ETF reached 126 million yuan today, representing a week-on-week increase of 50.90% [1] Trading Volume Summary - The Tianfu CSI 800 ETF (515800) had a trading volume of 105 million yuan today, an increase of 49 million yuan from the previous trading day, with a week-on-week growth of 87.52% [1] - The 800 Enhanced ETF (159517) recorded a trading volume of 6.9766 million yuan, up by 311,800 yuan from the previous trading day, reflecting a week-on-week increase of 4.68% [1] Market Performance Summary - As of market close, the CSI 800 Index (000906) rose by 2.03%, while the average increase of related ETFs tracking the CSI 800 Index was 2.20% [1] - The top performers included the 800 Enhanced ETF (159517) and the E Fund CSI 800 ETF (515810), which increased by 3.13% and 2.12% respectively [1] Detailed Trading Data - Trading data for CSI 800 Index ETFs on August 25: - Tianfu CSI 800 ETF (515800): Daily change of 1.97%, trading volume of 105 million yuan, increase of 49 million yuan, growth of 87.52% [1] - 800 Enhanced ETF (159517): Daily change of 3.13%, trading volume of 6.9766 million yuan, increase of 311,800 yuan, growth of 4.68% [1] - Penghua CSI 800 ETF (159800): Daily change of 1.60%, trading volume of 821,400 yuan, decrease of 209,970 yuan, decline of 71.88% [1] - E Fund CSI 800 ETF (515810): Daily change of 2.12%, trading volume of 13.3656 million yuan, decrease of 466,270 yuan, decline of 25.86% [1]

Group 1 - The current market is exhibiting characteristics of a "water buffalo" market, defined as a divergence between fundamentals and liquidity [1] - Historical analysis shows that such markets typically last no more than 4 months, and the sustainability of the current market rally depends on future improvements in fundamentals [1][3] - The market has transitioned from a stock-based to an incremental growth phase since June, with expectations for further policy support to enhance fundamental outlook [1] Group 2 - The liquidity-driven bull market can be divided into two phases: a rapid rotation phase and a sustained mainline phase [3] - In the rapid rotation phase, various sectors can lead, but the sustainability of these gains is weak, as seen in previous years [3] - The sustained mainline phase may see certain sectors improve due to policy support or industry cycles, despite overall fundamentals remaining weak [3] Group 3 - The A-share market is currently in a rapid rotation phase, with sectors like AI, innovative pharmaceuticals, new consumption, and infrastructure taking turns as hot topics [3][4] - Investors face challenges in selecting the right sectors due to the fast-paced market environment, making broad-based index investments a safer choice [4] Group 4 - The CSI A500 index offers a balanced industry allocation and includes both large-cap and small-cap stocks, providing a broader market coverage compared to the CSI 300 index [4][6] - The CSI A500 index has a higher content of new productive forces, with a reduced weight in traditional sectors like finance, allowing for greater growth potential [6][8] - Historical performance indicates that the CSI A500 index has outperformed the CSI 300 index in various market conditions, showcasing its adaptability [8][9] Group 5 - For ordinary investors, constructing a portfolio based on the CSI A500 index can help navigate the current volatile market environment [13] - A balanced approach combining equity and bond investments is recommended, with options like the CSI A500 ETF and ten-year government bond ETFs for stability and growth [14][15]

1. Report Industry Investment Rating - No relevant information provided 2. Core Viewpoints of the Report - The market overestimates the proportion of active equity funds underperforming their benchmarks. Selecting a benchmark that matches the fund manager's style can significantly reduce the proportion of funds with performance 10% lower than the benchmark [3][19] - The S&P 500 is the most widely - chosen benchmark for active equity funds in the US, and Russell style indices are also highly recognized [24][25] - In the short - term, banks, non - banks, and food and beverage are under - allocated industries, while electronics, media, and machinery are over - allocated industries. Active equity funds in Hong Kong still prefer growth - oriented industries [3][38][43] - When selecting a performance comparison benchmark, factors such as index style suitability, market recognition, stability, investment opportunities, and long - term viability should be considered [3] 3. Summary According to the Table of Contents 3.1 Market Overestimates the Proportion of Active Equity Funds Underperforming Their Benchmarks 3.1.1 Problems of Directly Selecting Broad - based Indices as Benchmarks - The two simple calculation methods currently used in the market may not have reference value for the future. The data on the proportion of funds underperforming benchmarks in the past has randomness and cannot reflect the true ability of active equity funds [10][11] - If fund managers choose broad - based indices as benchmarks without changing their investment strategies, the probability of their performance being more than 10% lower than the benchmark in three years is uncontrollable. Selecting a benchmark that matches the investment style is more important [16][19][20] 3.1.2 Benchmarks Used by Overseas Active Equity Funds - In the US, the S&P 500 is the most widely - chosen benchmark, with a scale proportion of over 40% in all active products investing in the US. Russell style indices such as Russell 1000 Growth and Russell 1000 Value are also highly recognized [24][25] - Different types of US active funds have different benchmark selection preferences. For example, the S&P 500 is commonly chosen for large - cap core products, while Russell 2000 is dominant in small - cap core products [26] 3.2 Short - term Market Transaction Expectations 3.2.1 Industry and Stock Dimensions: Under - allocation of Finance and Traditional Consumption, Over - allocation of Technology and Growth - Balanced style funds under - allocate non - banks, banks, and food and beverage, and over - allocate media, automobiles, and machinery. Growth style funds under - allocate food and beverage, transportation, and public utilities, and over - allocate electronics, power equipment, and machinery. Value style funds under - allocate banks, non - banks, and building decoration, and over - allocate power equipment, real estate, and pharmaceutical biology [33][34][35] - Different industry - themed funds also have different over - and under - allocation situations. Overall, banks, non - banks, and food and beverage are industries with large under - allocation amounts, while electronics, media, and machinery are industries with large over - allocation amounts [38] 3.2.2 Hong Kong Stock Allocation: Over - and Under - allocation Relative to the Hang Seng Index - In Hong Kong stocks, under - allocated industries include banks, non - banks, and commerce and retail, while over - allocated industries include pharmaceutical biology, media, and electronics. Active equity funds in Hong Kong still prefer growth - oriented industries [43] 3.3 How to Select Performance Comparison Benchmarks for Active Equity Funds 3.3.1 Indices Currently Issued by Mainstream Index Companies - Active equity funds commonly choose broad - based indices, industry - themed indices, and SmartBeta products as performance comparison benchmarks. The top 5 most - tracked indices are usually broad - based indices such as the CSI 300, CSI 800, Hang Seng Index, CSI 500, and CSI Hong Kong Stock Connect Composite [46] - Mainstream index companies issue three types of indices: broad - based indices, SmartBeta indices, and industry - themed indices, covering multiple markets and strategies [49] 3.3.2 Indices More Likely to Generate Excess Returns - Three factors affect a fund's excess returns: investment ability, investment breadth, and investment opportunities. Indices with wider coverage and more investment opportunities can generate higher excess returns, but their Beta may be weaker [53][57] 3.3.3 How to Select Broad - based Indices - From multiple perspectives such as industry allocation deviation, standard deviation of component stock returns, and stock - selection tolerance, the CSI A500 index is more in line with the investment styles of most fund managers [61]

- The report discusses the application of stock pair trading ideas in factor strategies, specifically focusing on reversal factors which have historically shown strong performance[1] - Traditional reversal factors include "N-month price reversal," "highest price length," and "volume ratio," which capture the trend that stocks with low past returns tend to perform better in the future and vice versa[1][2] - The report introduces a pair reversal factor that captures reversal opportunities between individual stocks within the same industry, differing from traditional pair trading by using periodic closing instead of stop-loss conditions[2][3] - The pair reversal factor is tested using a hedging strategy with a monthly rebalancing frequency, using the CSI 800 index constituents as the stock pool, and achieving an annualized excess return of 8% from 2007 to 2016[3][4] - The pair reversal factor is also applied to enhance multi-factor portfolios with weekly rebalancing, showing improved returns even after considering transaction costs, with a benchmark multi-factor portfolio return of 424.40% and a pair rebalancing portfolio return of 501.59% during the sample period from 2007 to 2016[4][5] Quantitative Models and Construction Methods 1. **Model Name**: Pair Reversal Factor - **Construction Idea**: Capture reversal opportunities between individual stocks within the same industry, similar to pair trading but with periodic closing instead of stop-loss conditions[2][3] - **Construction Process**: 1. Perform cointegration regression on the log prices of two assets to check for cointegration relationship[43][44] 2. Calculate the spread and standard deviation of the spread during the learning period[45][46] 3. Use the spread and standard deviation to determine the opening threshold and execute trades accordingly[46][49] 4. Rebalance the portfolio monthly by closing all positions and reopening new ones based on the updated spread and standard deviation[51][53] - **Evaluation**: The pair reversal factor effectively captures stock price reversals and mean reversion of price spreads, providing significant excess returns at the individual stock level[69] Model Backtest Results 1. **Pair Reversal Factor**: - **Annualized Return**: 31.17% (2007), 50.85% (2008), 51.19% (2009), 21.39% (2010), 14.26% (2011), 14.75% (2012), 25.75% (2013), 9.10% (2014), 59.01% (2015), 17.05% (2016), 1246.06% (full sample)[63] - **Maximum Drawdown**: 4.44% (2007), 4.62% (2008), 4.61% (2009), 2.97% (2010), 2.64% (2011), 2.23% (2012), 2.57% (2013), 4.99% (2014), 5.48% (2015), 4.07% (2016), 5.48% (full sample)[63] - **Win Rate**: 58.38% (2007), 60.57% (2008), 59.02% (2009), 58.26% (2010), 58.20% (2011), 59.66% (2012), 59.66% (2013), 51.02% (2014), 59.84% (2015), 59.43% (2016), 58.27% (full sample)[63] Quantitative Factors and Construction Methods 1. **Factor Name**: N-month Price Reversal - **Construction Idea**: Measure the price change over a fixed time window to capture the reversal effect[30][33] - **Construction Process**: 1. Calculate the price change over the past N months: $(\text{Current Price} - \text{Price N months ago}) / \text{Price N months ago}$[33] - **Evaluation**: Reversal factors have shown strong performance in historical studies, with high IC values and good performance in various metrics such as LS return, LS win rate, LS IR, IC IR, and IC P[33][35] Factor Backtest Results 1. **N-month Price Reversal**: - **IC**: -5.72% (1-month), -4.75% (3-month), -4.10% (6-month), -3.55% (12-month)[35] - **LS Return**: 21.84% (1-month), 20.33% (3-month), 18.13% (6-month), 17.66% (12-month)[35] - **LS Win Rate**: 64.41% (1-month), 59.32% (3-month), 56.78% (6-month), 61.02% (12-month)[35] - **LS IR**: 0.99 (1-month), 0.81 (3-month), 0.77 (6-month), 0.83 (12-month)[35] - **IC IR**: 0.72 (1-month), 0.92 (3-month), 0.78 (6-month), 0.83 (12-month)[35] - **IC P**: 0.0% (1-month), 0.2% (3-month), 0.5% (6-month), 1.1% (12-month)[35]